{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on Reddit News Headlines with Python’s Natural Language Toolkit (NLTK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython import display\n",
    "import math\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', context='talk', palette='Dark2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import praw\n",
    "\n",
    "reddit = praw.Reddit(client_id='KgFBUmRPl1whtw',\n",
    "                     client_secret='gmOJmuS3iDRwrUnhmn0CYtpnGGY',\n",
    "                     user_agent='LearnDataSci')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "headlines = set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "965\n"
     ]
    }
   ],
   "source": [
    "for submission in reddit.subreddit('politics').new(limit=None):\n",
    "    headlines.add(submission.title)\n",
    "    display.clear_output()\n",
    "    print(len(headlines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'compound': -0.5267,\n",
      "  'headline': 'DOJ watchdog reportedly sends criminal referral for McCabe to federal prosecutor',\n",
      "  'neg': 0.254,\n",
      "  'neu': 0.746,\n",
      "  'pos': 0.0},\n",
      " {'compound': 0.0,\n",
      "  'headline': 'House Dems add five candidates to ‘Red to Blue’ program',\n",
      "  'neg': 0.0,\n",
      "  'neu': 1.0,\n",
      "  'pos': 0.0},\n",
      " {'compound': 0.0,\n",
      "  'headline': 'DeveloperTown co-founder launches independent bid for U.S. Senate',\n",
      "  'neg': 0.0,\n",
      "  'neu': 1.0,\n",
      "  'pos': 0.0}]\n"
     ]
    }
   ],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
    "\n",
    "sia = SIA()\n",
    "results = []\n",
    "\n",
    "for line in headlines:\n",
    "    pol_score = sia.polarity_scores(line)\n",
    "    pol_score['headline'] = line\n",
    "    results.append(pol_score)\n",
    "\n",
    "pprint(results[:3], width=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>headline</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.5267</td>\n",
       "      <td>DOJ watchdog reportedly sends criminal referra...</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>House Dems add five candidates to ‘Red to Blue...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>DeveloperTown co-founder launches independent ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5267</td>\n",
       "      <td>Japanese PM Praises Trump for North Korea Brea...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Democrats Back 'Impeach Trump' Candidates, Pol...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compound                                           headline    neg    neu  \\\n",
       "0   -0.5267  DOJ watchdog reportedly sends criminal referra...  0.254  0.746   \n",
       "1    0.0000  House Dems add five candidates to ‘Red to Blue...  0.000  1.000   \n",
       "2    0.0000  DeveloperTown co-founder launches independent ...  0.000  1.000   \n",
       "3    0.5267  Japanese PM Praises Trump for North Korea Brea...  0.000  0.673   \n",
       "4    0.0000  Democrats Back 'Impeach Trump' Candidates, Pol...  0.000  1.000   \n",
       "\n",
       "     pos  \n",
       "0  0.000  \n",
       "1  0.000  \n",
       "2  0.000  \n",
       "3  0.327  \n",
       "4  0.000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_records(results)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>headline</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.5267</td>\n",
       "      <td>DOJ watchdog reportedly sends criminal referra...</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>House Dems add five candidates to ‘Red to Blue...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>DeveloperTown co-founder launches independent ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5267</td>\n",
       "      <td>Japanese PM Praises Trump for North Korea Brea...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Democrats Back 'Impeach Trump' Candidates, Pol...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compound                                           headline    neg    neu  \\\n",
       "0   -0.5267  DOJ watchdog reportedly sends criminal referra...  0.254  0.746   \n",
       "1    0.0000  House Dems add five candidates to ‘Red to Blue...  0.000  1.000   \n",
       "2    0.0000  DeveloperTown co-founder launches independent ...  0.000  1.000   \n",
       "3    0.5267  Japanese PM Praises Trump for North Korea Brea...  0.000  0.673   \n",
       "4    0.0000  Democrats Back 'Impeach Trump' Candidates, Pol...  0.000  1.000   \n",
       "\n",
       "     pos  label  \n",
       "0  0.000     -1  \n",
       "1  0.000      0  \n",
       "2  0.000      0  \n",
       "3  0.327      1  \n",
       "4  0.000      0  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'] = 0\n",
    "df.loc[df['compound'] > 0.2, 'label'] = 1\n",
    "df.loc[df['compound'] < -0.2, 'label'] = -1\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[['headline', 'label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('reddit_headlines_labels.csv', mode='a', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    433\n",
       "-1    332\n",
       " 1    200\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive headlines:\n",
      "\n",
      "['Japanese PM Praises Trump for North Korea Breakthrough',\n",
      " \"Bernie Sanders Joins Cory Booker's Marijuana Justice Act to Federally Legalize Weed\",\n",
      " 'Trump Administration Seeks to Expand Sales of Armed Drones',\n",
      " 'AP: Trump leaves open possibility of bailing on meeting with Kim, Trump supported by Japan',\n",
      " 'Trump skews reasons behind his 2016 win']\n",
      "\n",
      "Negative headlines:\n",
      "\n",
      "['DOJ watchdog reportedly sends criminal referral for McCabe to federal prosecutor',\n",
      " 'Beyer Statement On Syria Strikes',\n",
      " 'Trump confidantes Bossie, Lewandowski urge against firing Mueller',\n",
      " 'Mattis disputes report he wanted Congress to approve Syria strike',\n",
      " 'Criminal charges recommended for fired FBI official Andrew McCabe']\n"
     ]
    }
   ],
   "source": [
    "print(\"Positive headlines:\\n\")\n",
    "pprint(list(df[df['label'] == 1].headline)[:5], width=200)\n",
    "\n",
    "print(\"\\nNegative headlines:\\n\")\n",
    "pprint(list(df[df['label'] == -1].headline)[:5], width=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    44.870466\n",
       "-1    34.404145\n",
       " 1    20.725389\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts(normalize=True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20791d54e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "counts = df.label.value_counts(normalize=True) * 100\n",
    "\n",
    "sns.barplot(x=counts.index, y=counts, ax=ax)\n",
    "\n",
    "ax.set_xticklabels(['Negative', 'Neutral', 'Positive'])\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'is', 'an', 'example', 'sentence', '!', 'However', ',', 'it', 'is', \"n't\", 'a', 'very', 'informative', 'one']\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize, RegexpTokenizer\n",
    "\n",
    "example = \"This is an example sentence! However, it isn't a very informative one\"\n",
    "\n",
    "print(word_tokenize(example, language='english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'is', 'an', 'example', 'sentence', 'However', 'it', 'isn', 't', 'a', 'very', 'informative', 'one']\n"
     ]
    }
   ],
   "source": [
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "print(tokenizer.tokenize(example))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers']\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "stop_words = stopwords.words('english')\n",
    "print(stop_words[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_text(headlines):\n",
    "    tokens = []\n",
    "    for line in headlines:\n",
    "        line = line.lower()\n",
    "        toks = tokenizer.tokenize(line)\n",
    "        toks = [t for t in toks if t not in stop_words]\n",
    "        tokens.extend(toks)\n",
    "    \n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('trump', 80),\n",
       " ('says', 16),\n",
       " ('justice', 13),\n",
       " ('new', 13),\n",
       " ('senate', 12),\n",
       " ('york', 12),\n",
       " ('mueller', 11),\n",
       " ('comey', 11),\n",
       " ('support', 10),\n",
       " ('legal', 10),\n",
       " ('security', 9),\n",
       " ('white', 9),\n",
       " ('giuliani', 9),\n",
       " ('korea', 8),\n",
       " ('party', 8),\n",
       " ('donald', 8),\n",
       " ('cohen', 8),\n",
       " ('state', 8),\n",
       " ('like', 8),\n",
       " ('join', 8)]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_lines = list(df[df.label == 1].headline)\n",
    "\n",
    "pos_tokens = process_text(pos_lines)\n",
    "pos_freq = nltk.FreqDist(pos_tokens)\n",
    "\n",
    "pos_freq.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20791c6f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_val = [x[1] for x in pos_freq.most_common()]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.plot(y_val)\n",
    "\n",
    "plt.xlabel(\"Words\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Word Frequency Distribution (Positive)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20793988940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_final = []\n",
    "for i, k, z, t in zip(y_val[0::4], y_val[1::4], y_val[2::4], y_val[3::4]):\n",
    "    y_final.append(math.log(i + k + z + t))\n",
    "\n",
    "x_val = [math.log(i + 1) for i in range(len(y_final))]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.xlabel(\"Words (Log)\")\n",
    "plt.ylabel(\"Frequency (Log)\")\n",
    "plt.title(\"Word Frequency Distribution (Positive)\")\n",
    "plt.plot(x_val, y_final)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('trump', 125),\n",
       " ('mueller', 25),\n",
       " ('criminal', 21),\n",
       " ('judge', 20),\n",
       " ('mccabe', 19),\n",
       " ('court', 18),\n",
       " ('contempt', 17),\n",
       " ('police', 16),\n",
       " ('comey', 16),\n",
       " ('pittsburgh', 15),\n",
       " ('new', 15),\n",
       " ('kobach', 15),\n",
       " ('syria', 14),\n",
       " ('war', 14),\n",
       " ('senate', 13),\n",
       " ('u', 13),\n",
       " ('cohen', 13),\n",
       " ('case', 13),\n",
       " ('fires', 12),\n",
       " ('says', 12)]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_lines = list(df2[df2.label == -1].headline)\n",
    "\n",
    "neg_tokens = process_text(neg_lines)\n",
    "neg_freq = nltk.FreqDist(neg_tokens)\n",
    "\n",
    "neg_freq.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207938b59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_val = [x[1] for x in neg_freq.most_common()]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.plot(y_val)\n",
    "\n",
    "plt.xlabel(\"Words\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Word Frequency Distribution (Negative)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207939eb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_final = []\n",
    "for i, k, z in zip(y_val[0::3], y_val[1::3], y_val[2::3]):\n",
    "    if i + k + z == 0:\n",
    "        break\n",
    "    y_final.append(math.log(i + k + z))\n",
    "\n",
    "x_val = [math.log(i+1) for i in range(len(y_final))]\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.xlabel(\"Words (Log)\")\n",
    "plt.ylabel(\"Frequency (Log)\")\n",
    "plt.title(\"Word Frequency Distribution (Negative)\")\n",
    "plt.plot(x_val, y_final)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
